Considerations for a women’s rehabilitation programme following anterior cruciate ligament reconstruction: a concept mapping approach to enhance women’s outcomes
Bibliographic record
Abstract
OBJECTIVE: To identify gender/sex-specific considerations to enhance anterior cruciate ligament (ACL) rupture rehabilitation experiences and outcomes among women. METHODS: Mixed-methods concept mapping. 19 women 1-3 years post-ACL rupture and 28 rehabilitation practitioners (68% physiotherapists) who regularly treat women following ACL rupture brainstormed statements to a prompt ('What factors should be addressed in ACL rehabilitation for women (18-45 years)?') before thematically sorting and rating the statements for importance and feasibility (5-point Likert scales). RESULTS: Ninety unique statements were brainstormed, sorted and rated. A seven-cluster solution was identified-from most to least important (number of statements, cluster mean importance/5)-1. foster goal-driven rehabilitation (18, 3.98); 2. promote mental and emotional well-being (13, 3.96); 3. create adaptable and supportive environments (10, 3.74); 4. provide education and resources (16, 3.73); 5. engage the whole team for the whole woman (13, 3.52); 6. address accessibility and competing demands (8, 3.36) and 7. build peer, group and social support (12, 3.22). 'Goal-driven rehabilitation' was deemed the most, and 'peer, group and social support' the least feasible cluster to address in women-specific ACL rehabilitation. CONCLUSION: Enhancing ACL rehabilitation strategies could reduce the gender/sex disparity for women most at risk of inferior short- and long-term outcomes. Practitioners can leverage our seven-cluster solution for practical guidance in creating supportive and empowering environments that prioritise collaboration and active listening, enabling women-centred goal-driven rehabilitation practices. Lower-rated clusters, such as peer, group and social support, may remain crucial, as they reflect known influences on rehabilitation motivation and adherence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".